automatic import of python-gower

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diff --git a/.gitignore b/.gitignore
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--- a/.gitignore
+++ b/.gitignore
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+/gower-0.1.2.tar.gz
diff --git a/python-gower.spec b/python-gower.spec
new file mode 100644
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+%global _empty_manifest_terminate_build 0
+Name:		python-gower
+Version:	0.1.2
+Release:	1
+Summary:	Python implementation of Gowers distance, pairwise between records in two data sets
+License:	MIT
+URL:		https://github.com/wwwjk366/gower
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/7c/b8/f02ffa72009105e981b21fe957895107d1b3c81dece43167d28d8acfdfb0/gower-0.1.2.tar.gz
+BuildArch:	noarch
+
+Requires:	python3-numpy
+Requires:	python3-scipy
+
+%description
+<!-- badges: start -->
+[![Build Status](https://travis-ci.com/wwwjk366/gower.svg?branch=master)](https://travis-ci.com/wwwjk366/gower)
+[![PyPI version](https://badge.fury.io/py/gower.svg)](https://pypi.org/project/gower/)
+[![Downloads](https://pepy.tech/badge/gower/month)](https://pepy.tech/project/gower/month)
+<!-- badges: end -->
+
+# Introduction
+
+Gower's distance calculation in Python. Gower Distance is a distance measure that can be used to calculate distance between two entity whose attribute has a mixed of categorical and numerical values. [Gower (1971) A general coefficient of similarity and some of its properties. Biometrics 27 857–874.](https://www.jstor.org/stable/2528823?seq=1) 
+
+More details and examples can be found on my personal website here:(https://www.thinkdatascience.com/post/2019-12-16-introducing-python-package-gower/)
+
+Core functions are wrote by [Marcelo Beckmann](https://sourceforge.net/projects/gower-distance-4python/files/).
+
+# Examples
+
+## Installation
+
+```
+pip install gower
+```
+
+## Generate some data
+
+```python
+import numpy as np
+import pandas as pd
+import gower
+
+Xd=pd.DataFrame({'age':[21,21,19, 30,21,21,19,30,None],
+'gender':['M','M','N','M','F','F','F','F',None],
+'civil_status':['MARRIED','SINGLE','SINGLE','SINGLE','MARRIED','SINGLE','WIDOW','DIVORCED',None],
+'salary':[3000.0,1200.0 ,32000.0,1800.0 ,2900.0 ,1100.0 ,10000.0,1500.0,None],
+'has_children':[1,0,1,1,1,0,0,1,None],
+'available_credit':[2200,100,22000,1100,2000,100,6000,2200,None]})
+Yd = Xd.iloc[1:3,:]
+X = np.asarray(Xd)
+Y = np.asarray(Yd)
+
+```
+
+## Find the distance matrix
+
+```python
+gower.gower_matrix(X)
+```
+
+
+
+
+    array([[0.        , 0.3590238 , 0.6707398 , 0.31787416, 0.16872811,
+            0.52622986, 0.59697855, 0.47778758,        nan],
+           [0.3590238 , 0.        , 0.6964303 , 0.3138769 , 0.523629  ,
+            0.16720603, 0.45600235, 0.6539635 ,        nan],
+           [0.6707398 , 0.6964303 , 0.        , 0.6552807 , 0.6728013 ,
+            0.6969697 , 0.740428  , 0.8151941 ,        nan],
+           [0.31787416, 0.3138769 , 0.6552807 , 0.        , 0.4824794 ,
+            0.48108295, 0.74818605, 0.34332284,        nan],
+           [0.16872811, 0.523629  , 0.6728013 , 0.4824794 , 0.        ,
+            0.35750175, 0.43237334, 0.3121036 ,        nan],
+           [0.52622986, 0.16720603, 0.6969697 , 0.48108295, 0.35750175,
+            0.        , 0.2898751 , 0.4878362 ,        nan],
+           [0.59697855, 0.45600235, 0.740428  , 0.74818605, 0.43237334,
+            0.2898751 , 0.        , 0.57476616,        nan],
+           [0.47778758, 0.6539635 , 0.8151941 , 0.34332284, 0.3121036 ,
+            0.4878362 , 0.57476616, 0.        ,        nan],
+           [       nan,        nan,        nan,        nan,        nan,
+                   nan,        nan,        nan,        nan]], dtype=float32)
+
+
+## Find Top n results
+
+```python
+gower.gower_topn(Xd.iloc[0:2,:], Xd.iloc[:,], n = 5)
+```
+
+
+
+
+    {'index': array([4, 3, 1, 7, 5]),
+     'values': array([0.16872811, 0.31787416, 0.3590238 , 0.47778758, 0.52622986],
+           dtype=float32)}
+
+
+
+
+%package -n python3-gower
+Summary:	Python implementation of Gowers distance, pairwise between records in two data sets
+Provides:	python-gower
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-gower
+<!-- badges: start -->
+[![Build Status](https://travis-ci.com/wwwjk366/gower.svg?branch=master)](https://travis-ci.com/wwwjk366/gower)
+[![PyPI version](https://badge.fury.io/py/gower.svg)](https://pypi.org/project/gower/)
+[![Downloads](https://pepy.tech/badge/gower/month)](https://pepy.tech/project/gower/month)
+<!-- badges: end -->
+
+# Introduction
+
+Gower's distance calculation in Python. Gower Distance is a distance measure that can be used to calculate distance between two entity whose attribute has a mixed of categorical and numerical values. [Gower (1971) A general coefficient of similarity and some of its properties. Biometrics 27 857–874.](https://www.jstor.org/stable/2528823?seq=1) 
+
+More details and examples can be found on my personal website here:(https://www.thinkdatascience.com/post/2019-12-16-introducing-python-package-gower/)
+
+Core functions are wrote by [Marcelo Beckmann](https://sourceforge.net/projects/gower-distance-4python/files/).
+
+# Examples
+
+## Installation
+
+```
+pip install gower
+```
+
+## Generate some data
+
+```python
+import numpy as np
+import pandas as pd
+import gower
+
+Xd=pd.DataFrame({'age':[21,21,19, 30,21,21,19,30,None],
+'gender':['M','M','N','M','F','F','F','F',None],
+'civil_status':['MARRIED','SINGLE','SINGLE','SINGLE','MARRIED','SINGLE','WIDOW','DIVORCED',None],
+'salary':[3000.0,1200.0 ,32000.0,1800.0 ,2900.0 ,1100.0 ,10000.0,1500.0,None],
+'has_children':[1,0,1,1,1,0,0,1,None],
+'available_credit':[2200,100,22000,1100,2000,100,6000,2200,None]})
+Yd = Xd.iloc[1:3,:]
+X = np.asarray(Xd)
+Y = np.asarray(Yd)
+
+```
+
+## Find the distance matrix
+
+```python
+gower.gower_matrix(X)
+```
+
+
+
+
+    array([[0.        , 0.3590238 , 0.6707398 , 0.31787416, 0.16872811,
+            0.52622986, 0.59697855, 0.47778758,        nan],
+           [0.3590238 , 0.        , 0.6964303 , 0.3138769 , 0.523629  ,
+            0.16720603, 0.45600235, 0.6539635 ,        nan],
+           [0.6707398 , 0.6964303 , 0.        , 0.6552807 , 0.6728013 ,
+            0.6969697 , 0.740428  , 0.8151941 ,        nan],
+           [0.31787416, 0.3138769 , 0.6552807 , 0.        , 0.4824794 ,
+            0.48108295, 0.74818605, 0.34332284,        nan],
+           [0.16872811, 0.523629  , 0.6728013 , 0.4824794 , 0.        ,
+            0.35750175, 0.43237334, 0.3121036 ,        nan],
+           [0.52622986, 0.16720603, 0.6969697 , 0.48108295, 0.35750175,
+            0.        , 0.2898751 , 0.4878362 ,        nan],
+           [0.59697855, 0.45600235, 0.740428  , 0.74818605, 0.43237334,
+            0.2898751 , 0.        , 0.57476616,        nan],
+           [0.47778758, 0.6539635 , 0.8151941 , 0.34332284, 0.3121036 ,
+            0.4878362 , 0.57476616, 0.        ,        nan],
+           [       nan,        nan,        nan,        nan,        nan,
+                   nan,        nan,        nan,        nan]], dtype=float32)
+
+
+## Find Top n results
+
+```python
+gower.gower_topn(Xd.iloc[0:2,:], Xd.iloc[:,], n = 5)
+```
+
+
+
+
+    {'index': array([4, 3, 1, 7, 5]),
+     'values': array([0.16872811, 0.31787416, 0.3590238 , 0.47778758, 0.52622986],
+           dtype=float32)}
+
+
+
+
+%package help
+Summary:	Development documents and examples for gower
+Provides:	python3-gower-doc
+%description help
+<!-- badges: start -->
+[![Build Status](https://travis-ci.com/wwwjk366/gower.svg?branch=master)](https://travis-ci.com/wwwjk366/gower)
+[![PyPI version](https://badge.fury.io/py/gower.svg)](https://pypi.org/project/gower/)
+[![Downloads](https://pepy.tech/badge/gower/month)](https://pepy.tech/project/gower/month)
+<!-- badges: end -->
+
+# Introduction
+
+Gower's distance calculation in Python. Gower Distance is a distance measure that can be used to calculate distance between two entity whose attribute has a mixed of categorical and numerical values. [Gower (1971) A general coefficient of similarity and some of its properties. Biometrics 27 857–874.](https://www.jstor.org/stable/2528823?seq=1) 
+
+More details and examples can be found on my personal website here:(https://www.thinkdatascience.com/post/2019-12-16-introducing-python-package-gower/)
+
+Core functions are wrote by [Marcelo Beckmann](https://sourceforge.net/projects/gower-distance-4python/files/).
+
+# Examples
+
+## Installation
+
+```
+pip install gower
+```
+
+## Generate some data
+
+```python
+import numpy as np
+import pandas as pd
+import gower
+
+Xd=pd.DataFrame({'age':[21,21,19, 30,21,21,19,30,None],
+'gender':['M','M','N','M','F','F','F','F',None],
+'civil_status':['MARRIED','SINGLE','SINGLE','SINGLE','MARRIED','SINGLE','WIDOW','DIVORCED',None],
+'salary':[3000.0,1200.0 ,32000.0,1800.0 ,2900.0 ,1100.0 ,10000.0,1500.0,None],
+'has_children':[1,0,1,1,1,0,0,1,None],
+'available_credit':[2200,100,22000,1100,2000,100,6000,2200,None]})
+Yd = Xd.iloc[1:3,:]
+X = np.asarray(Xd)
+Y = np.asarray(Yd)
+
+```
+
+## Find the distance matrix
+
+```python
+gower.gower_matrix(X)
+```
+
+
+
+
+    array([[0.        , 0.3590238 , 0.6707398 , 0.31787416, 0.16872811,
+            0.52622986, 0.59697855, 0.47778758,        nan],
+           [0.3590238 , 0.        , 0.6964303 , 0.3138769 , 0.523629  ,
+            0.16720603, 0.45600235, 0.6539635 ,        nan],
+           [0.6707398 , 0.6964303 , 0.        , 0.6552807 , 0.6728013 ,
+            0.6969697 , 0.740428  , 0.8151941 ,        nan],
+           [0.31787416, 0.3138769 , 0.6552807 , 0.        , 0.4824794 ,
+            0.48108295, 0.74818605, 0.34332284,        nan],
+           [0.16872811, 0.523629  , 0.6728013 , 0.4824794 , 0.        ,
+            0.35750175, 0.43237334, 0.3121036 ,        nan],
+           [0.52622986, 0.16720603, 0.6969697 , 0.48108295, 0.35750175,
+            0.        , 0.2898751 , 0.4878362 ,        nan],
+           [0.59697855, 0.45600235, 0.740428  , 0.74818605, 0.43237334,
+            0.2898751 , 0.        , 0.57476616,        nan],
+           [0.47778758, 0.6539635 , 0.8151941 , 0.34332284, 0.3121036 ,
+            0.4878362 , 0.57476616, 0.        ,        nan],
+           [       nan,        nan,        nan,        nan,        nan,
+                   nan,        nan,        nan,        nan]], dtype=float32)
+
+
+## Find Top n results
+
+```python
+gower.gower_topn(Xd.iloc[0:2,:], Xd.iloc[:,], n = 5)
+```
+
+
+
+
+    {'index': array([4, 3, 1, 7, 5]),
+     'values': array([0.16872811, 0.31787416, 0.3590238 , 0.47778758, 0.52622986],
+           dtype=float32)}
+
+
+
+
+%prep
+%autosetup -n gower-0.1.2
+
+%build
+%py3_build
+
+%install
+%py3_install
+install -d -m755 %{buildroot}/%{_pkgdocdir}
+if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
+if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
+if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
+if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
+pushd %{buildroot}
+if [ -d usr/lib ]; then
+	find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+if [ -d usr/lib64 ]; then
+	find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+if [ -d usr/bin ]; then
+	find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+if [ -d usr/sbin ]; then
+	find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+touch doclist.lst
+if [ -d usr/share/man ]; then
+	find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
+fi
+popd
+mv %{buildroot}/filelist.lst .
+mv %{buildroot}/doclist.lst .
+
+%files -n python3-gower -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Tue Apr 11 2023 Python_Bot <Python_Bot@openeuler.org> - 0.1.2-1
+- Package Spec generated
diff --git a/sources b/sources
new file mode 100644
index 0000000..b2c214c
--- /dev/null
+++ b/sources
@@ -0,0 +1 @@
+1d33bdd101ad7196dbadad0fc09de08c  gower-0.1.2.tar.gz